Understanding Agentic Systems: Workflows vs. Agents

Agentic Systems: AI-driven setups where multiple agents work in tandem to achieve goals autonomously.

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This is the essence of agentic systems, where the core principles are: 

1. Complete autonomy 
2. Goal-oriented behavior 
3. Adaptability based on real-time information 

Agentic systems are not designed to offer insights. They are designed to take action without constant oversight. They work towards achieving defined goals and adapt the behavior based on real-time data and feedback. This positions agentic systems to be great at decision-making while maintaining unprecedented speed and quality. 

Understanding AI Agents and Agentic Workflows 

AI agents are software programs that take action with minimal human intervention. The agents can be simple rule-based systems (like invoice data extraction) or advanced ML-charged programs that adapt over time.

Agentic workflows, on the other hand, coordinate one or more such AI agents to handle complex goals from start to finish. AI agents alone could handle straightforward tasks, but when multiple such agents work in unison, their collective capabilities enable the handling of much more complex tasks. Multiple agents can collaborate and pass the task among themselves to complete agentic workflows. 

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The workflow might even spawn a multi-agent system (MAS), where a primary agent (often called a meta-agent or orchestrator) delegates sub-tasks to other specialized agents​. All these agents work in parallel or sequence and share information (via a common memory or knowledge store) to stay aligned​. This mechanism mirrors human problem-solving – the system can plan, reason, and even backtrack to correct errors, rather than just following a rigid script. 

Comparative Analysis: Agentic Workflows vs. AI Agents

Agentic workflows and AI agents are closely related concepts, but they differ in scope and how they operate within automated systems. At a high level, an AI agent is a single autonomous entity designed to perform tasks, whereas an agentic workflow is a coordinated process (often involving multiple agents and tools) arranged to achieve a larger objective. 

Below, we compare their functionality, adaptability, and implementation. 

Scope and Complexity

An AI agent is built to autonomously handle specific tasks or a defined range of problems​. For example, one AI agent in finance might specialize in detecting credit card fraud, and that’s its sole job. In contrast, an agentic workflow orchestrates multiple steps or sub-tasks, potentially using multiple agents in concert, to accomplish a broader goal that would be too complex for a single agent​.

An AI agent can be considered a skilled individual worker, whereas an agentic workflow is like an entire team or assembly line working on a project. In an agentic workflow, a meta-agent (or controller) often oversees the process, breaking the overall goal into parts and assigning those to various agents or functional modules​

Adaptability and Decision-Making

AI agents and agentic workflows aim to be adaptive but in different ways. A standalone AI agent often adapts by learning from more data or adjusting to feedback within its task domain over time. 

For instance, a synthetic identity fraud detection agent might update its anomaly detection model as it encounters new fraud patterns. An agentic workflow, on the other hand, is adaptive at the process level. It can dynamically reconfigure the sequence of actions or delegate tasks based on current conditions. 

For example, if an agent in the workflow finds incomplete data for a loan application, the workflow could pivot to invoke another agent to fetch missing information, or it could pause and request human input, then resume. This means an agentic system can monitor and act simultaneously, potentially making it more responsive in time-critical situations. ‍

Implementation and Maintenance

Deploying a single AI agent versus an agentic workflow can differ in practice. 

A single agent might be implemented as one AI model or program with an apparent input-output behavior (e.g., a credit-scoring AI that takes applicant data and outputs a risk score). It can be integrated into systems relatively straightforwardly as a component.

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Agentic workflows, by contrast, require a framework for orchestration, such as a workflow engine or an agent platform, to manage multiple agents, tools, and data flows. They are essentially systems of systems, which can make them more complex to build and test. Ensuring all the agents in a workflow cooperate correctly (and securely) involves dealing with additional challenges: inter-agent communication protocols, unified memory or state management, conflict resolution if two agents’ actions collide, etc. This complexity can lead to unpredictable behavior if not carefully designed​. 

A well-designed agentic workflow can be easier to extend in terms of adaptability. If a new subtask arises, you might plug another agent or tool into the workflow without redesigning the system. Single agents are less modular in that sense (they do what they’re programmed to do; anything beyond that might require retraining or redeveloping that agent).

In finance, finding the right mix is key: sometimes, a single well-trained agent is the perfect solution for a targeted task, and sometimes, a collection of agents under an agentic workflow is needed for a complex operation. Organizations increasingly use a combination of both, where individual agents handle specific functions and agentic orchestration ties them into a powerful, adaptable whole.

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AI Agents and AI APIs

A critical factor in AI agents' effectiveness is their ability to leverage real-time data and external tools—this is where AI APIs come into play. 

AI agents are not supposed to be closed systems. In software contexts, an agentic system will actively collect data from various sources, such as web services, APIs, databases, and live feeds, to build its situational awareness​. 

APIs serve as bridges connecting AI agents to updated information. For instance, a fraud detection agent might query an identity verification API to validate a user’s details in real-time. 

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By calling these APIs on the fly, AI agents augment their knowledge beyond their initial training data, constantly refreshing their worldview with current information. This real-time data access is essential for informed decision-making. 

Each API connection gives the agent another insight or the ability to take a new action. This is how agentic systems achieve a form of “wide” intelligence, drawing on many data sources and services. The result is improved decision-making and responsiveness: By continuously ingesting real-time data through APIs, AI agents maintain an up-to-date understanding of their environment and can respond to changes immediately. 

The Future Roadmap for Agentic Systems

The trend to siphon all efforts toward intelligent automation has been in practice for a while. In the financial sector, intelligent automation has proved helpful in document fraud, identity verification, risk scoring, automated underwriting, financial statement analysis, transaction monitoring—the list goes on. 

The early success indicates that as the technology matures, agentic systems will deliver more innovative solutions and be able to stitch complex workflows. However, there is a caveat. 

New Challenges and Considerations with the Advancement of Agentic Systems 

One primary consideration is governance and control: giving AI agents more autonomy necessitates robust oversight mechanisms to ensure they act within ethical and regulatory boundaries. We can expect an increased regulatory focus on AI-driven decision-making in finance, with frameworks emerging to audit and validate the actions of autonomous agents​. 

Financial institutions will need to implement 

1. Transparency (to explain AI decisions),
2. Fail-safes (to intervene or shut down agents if they behave undesirably),
3. Security measures (to prevent manipulation of AI agents via adversarial attacks or data poisoning). 

Another challenge is technical complexity—deploying reliable multi-agent workflows at scale. This requires investment in infrastructure and new skills to design and maintain these systems. There may also be integration hurdles with legacy systems, which many banks still rely on​.

Agentic AI is too great to ignore.

Nonetheless, industry momentum suggests these challenges are being actively addressed, as the potential upside of agentic AI is too significant to ignore. 

Looking ahead, we anticipate converging deterministic automation and probabilistic AI agents into hybrid workflows that offer the best of both worlds: the consistency of rule-based systems and the flexibility of learning agents​. 

In finance, this could mean AI agents handling the decision-making and adaptation while traditional software robots handle fixed-format transactions under the agents’ guidance. 

We also foresee improvements in the underlying AI models (e.g., more powerful LLMs or specialized learning models), making agents more capable and context-aware. We also see better tooling for orchestrating agentic workflows (perhaps standardized platforms to deploy and monitor swarms of agents safely). 

Conclusion

In conclusion, agentic workflows cannot survive without AI agents. Agentic systems must take a significant leap in automation, moving from “computing and informing” to “deciding and acting.” 

This is a game-changer in finance, where decisions must be fast and well-founded. Agentic AI will continue transforming financial operations, making them more efficient, personalized, and resilient. 

The journey is just beginning, and navigating it will require careful balancing of innovation with governance. Those institutions that harness agentic AI effectively will likely gain a competitive edge, offering more innovative services and responding to market changes with agility. 

The future of finance will likely feature AI agents as integral collaborators, driving the industry toward greater automation and intelligence. At the same time, we work to ensure this revolution unfolds safely and responsibly.

If you’d like to discuss the prospects of Agentic AI for your financial institution, connect with us today!